1. Technical Field
The present disclosure relates to a device that detects a human eyeblink (blinking), and more particularly to a device that detects an eyeblink in the waveform of a human electrooculogram (EOG).
2. Description of Related Art
A technology for detecting a human eyeblink is proposed for the purpose of detecting the drowsiness of a human because the frequency of, and a change in, human eyeblinks are associated with the level of the drowsiness of a human. One of the known methods for detecting a human eyeblink include a method that captures the eyelids of a human and detects the opening or closing of the eyelids in the captured image. Another known method is an electrooculogram (EOG) method that, with the electrodes attached around the eyes, detects the potential difference (eye potential) between the cornea and the retina as the eyelids are opened or closed. The outline of the EOG method is as follows. When the eyelids are opened or closed, a temporary eye potential change occurs as schematically shown in FIG. 6A. The EOG method detects such an eye potential change in the eye potential data, measured on a time-series basis, for detecting an eyeblink. In regard to this point, “A computerized identification and data analysis of eyeblink EOG waves” by Hiroaki YUZE & Hideoki TADA, Japan Ergonomics Society Vol. 30, No. 5, pp. 331-337, proposes an algorithm for automatically detecting an eye potential change that is caused when an eyeblink occurs. This algorithm calculates the differential values of eye potential time-series data and then, as an eyeblink, detects a part of the waveform where the differential value exceeds the positive side threshold and the negative side threshold in a row in the waveform of the eye potential differential values within a predetermined time (about 0.2 seconds) as shown in FIG. 6B.
In the differential values of eye potential time-series data in the EOG method described above, the amplitude of the eyeblink waveform varies according to each subject. Therefore, according to the method described in “A computerized identification and data analysis of eyeblink EOG waves” by Hiroaki YUZE & Hideoki TADA, Japan Ergonomics Society Vol. 30, No. 5, pp. 331-337, the negative side threshold and the positive side threshold, used for detecting an eyeblink waveform, are set for each subject as follows, using the average value and the standard deviation of the differential values of the eye potential time-series data of each subject. That is, the negative side threshold is set to the “average value of differential values−2×standard deviation”, and the positive side threshold to the “average value of differential values+1×standard deviation”. This means that the thresholds are changed according to a difference in the amplitude of eyeblink waveforms. In regard to this point, a study by the inventor of the present disclosure reveals that, when the thresholds are set uniformly based on a constant times the standard deviation of the eye potential differential values of each subject (more exactly, though the difference of the threshold from the average value of the differential values is set to a constant times the standard deviation, the threshold is set virtually to a constant times the standard deviation because the average value of the differential values is approximately 0), it sometimes becomes difficult to accurately detect the eyeblink waveform depending upon some subjects. One of the reasons is that the eyeblink occurrence interval, that is, the occurrence frequency per unit time, differs from subject to subject and, therefore, the ratio of the standard deviation of the differential values of eye potential time-series data to the amplitude of the eyeblink waveform differs from subject to subject. Another reason is that, as described in “A computerized identification and data analysis of eyeblink EOG waves” by Hiroaki YUZE & Hideoki TADA, Japan Ergonomics Society Vol. 30, No. 5, pp. 331-337, the eye potential time-series data includes not only a potential change caused by an eyeblink (eyeblink waveform) but also a potential change caused by a factor other than an eyeblink such as an eyeball movement, with the result that a potential change in the data, caused by a factor other than an eyeblink, is sometimes detected erroneously as an eyeblink waveform.
A further study by the inventor of the present disclosure reveals that using the thresholds, which are set considering the frequency characteristic of the differential values of eye potential time-series data, makes it possible to detect an eyeblink waveform more accurately than before, with fewer detection errors and fewer detection omissions as compared to those before. This knowledge is used in the present disclosure.